Analyzing the reliability and effectiveness of public school evaluation system in Qatar

In 2004, Qatar government launched a huge education reform, Education for New Era (EFNE) which introduced changes to the K-12 educational system. The reform model suggested by Research and Development Cooperation (RAND) include three new government institutes; the Supreme Education Council (SEC) – now turned into the Ministry of Education and Higher Education (MoEHE) –, the education institute, and the evaluation institute. Although the two main institutes have a lot of interactions in their roles and duties related to public school evaluation system (PSES) they have a lack of systematic communication towards the whole PSES. The aim of this project is to analyze the PSES in terms of its reliability and effectiveness under the responsibility of the evaluation institute (EVI). The study focuses on the accuracy and consistency of the process of evaluating a school and studying the after evaluation action plans that enhance school monitoring of improvement resulting from evaluation feedback to proven system effectiveness. The project results shows lacking in reliability of school evaluation system in terms of its accuracy in areas of school evaluation, the use of QNEA results, and the process of evaluation. The reliability of the PSES is criticized in its consistency of practice were the process lack consistency in terms of common understanding of areas and standards that schools are evaluated according to. The effectiveness of the PSES is criticized as it does not provide a systematic approach to use the evaluation results for school improvement It is recommended that EVI considers a holistic evaluation system that combines school evaluation, school self-review, school leaders’ evaluation, the evaluation of school teachers and students’ assessments together so that they can insure its reliability. To obtain a higher level of reliability of evaluation system delivery and outcomes of PSES should be a result of a collaboration between EDI and EVI to solve issuers related to contradictions in authorities, responsibilities, and conclusions. On the other hand, the effectiveness of the school evaluation system could be improved by enhancing the monitoring and evaluation system and developing a system that manages implementing changes within the school

Multi-Layer Multi-Configuration Time-Dependent Hartree (ML-MCTDH) Approach to the Correlated Exciton-Vibrational Dynamics in the FMO Complex

The coupled quantum dynamics of excitonic and vibrational degrees of freedom is investigated for high-dimensional models of the Fenna-Matthews-Olson (FMO) complex. This includes a seven and an eight-site model with 518 and 592 harmonic vibrational modes, respectively. The coupling between local electronic transitions and vibrations is described within the Huang-Rhys model using parameters that are obtained by discretization of an experimental spectral density. Different pathways of excitation energy flow are analyzed in terms of the reduced one-exciton density matrix, focussing on the role of vibrational and vibronic excitation. Distinct features due to both competing time scales of vibrational and exciton motion and vibronically-assisted transfer are observed. The question of the effect of initial state preparation is addressed by comparing the case of an instantaneous Franck-Condon excitation at a single site with that of a laser field excitation.Comment: revised versio

The Effect of Site-Specific Spectral Densities on the High-Dimensional Exciton-Vibrational Dynamics in the FMO Complex

The coupled exciton-vibrational dynamics of a three-site model of the FMO complex is investigated using the Multi-layer Multi-configuration Time-dependent Hartree (ML-MCTDH) approach. Emphasis is put on the effect of the spectral density on the exciton state populations as well as on the vibrational and vibronic non-equilibrium excitations. Models which use either a single or site-specific spectral densities are contrasted to a spectral density adapted from experiment. For the transfer efficiency, the total integrated Huang-Rhys factor is found to be more important than details of the spectral distributions. However, the latter are relevant for the obtained non-equilibrium vibrational and vibronic distributions and thus influence the actual pattern of population relaxation.Comment: revised versio

Date Pits Based Nanomaterials For Thermal Insulation Applications - Towards Energy Efficient Buildings In Qatar

Air-conditioning systems make the most significant part of energy consumption in the residential sector. There is no denying that it is essential to produce a comfortable indoor thermal environment for residents in a building. The actual goal is to achieve thermal comfort level without putting too much cost on the ecological system. An effective way to help achieve such a goal is by incorporating thermal insulation in buildings. Thermal insulations help reduce thermal energy gained during the implementation of a desired thermal comfort level. This project aims to study a new, environmentally friendly nanomaterial containing nanoparticle of date-pits to create thermal insulations. In addition, fly ash and different ratios of the nanoparticle of date pits and sand composite were investigated. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) were used to characterize the new materials. The material with nanoparticle of date pits and 50% by-volume epoxy provide good thermal insulation with thermal conductivity of 0.26 !$"# that may be used in existing buildings. This has the potential to reduce the overall energy consumption by 4,494 %!ℎ and thereby to reduce '() emissions of a 570 ") house by 1.8 tons annually. The use of fly ash as an insulation material was not found to be as efficient compared to nanoparticle of date pits. In conclusion, the future of using nanoparticle of date pits in construction is bright and promising due to their promising initial results.يعتبر حافظ الطاقة في المباني، من أكثر المجالات أهمية في الوقت الراهن، ويرجع ذلك لارتفاع استهلاك الطاقة في القطاع المنزلي، نتيجة لاتخدام مكيفات الهواء، ووسائل التبريد، لتأمين الارتياح الحراري في البيئة الحرارية المحيطة بالإنسان. يعد استعمال المواد العازلة حراريا في المباني من أنجح الوسائل المتبعة تقنيا و اقتصاديا وبيئياً

Financial Fraud Detection using Machine Learning Techniques

Payments related fraud is a key aspect of cyber-crime agencies and recent research has shown that machine learning techniques can be applied successfully to detect fraudulent transactions in large amounts of payments data. Such techniques have the ability to detect fraudulent transactions that human auditors may not be able to catch and also do this on a real time basis. In this project, we apply multiple supervised machine learning techniques to the problem of fraud detection using a publicly available simulated payment transactions data. We aim to demonstrate how supervised ML techniques can be used to classify data with high class imbalance with high accuracy. We demonstrate that exploratory analysis can be used to separate fraudulent and nonfraudulent transactions. We also demonstrate that for a well separated dataset, treebased algorithms like Random Forest work much better than Logistic Regression

Expert Weighting Based Dynamic Eco-efficiency Assessment of World Consumption

Optimizing the consumption of natural resources and ensuring the availability of resources for both current and future generations has been the target for sustainability research. This paper aims to assess the eco-efficiency of global resource consumption through the environmental footprint perspective. The study effectively utilized EXIOBASE 3.41, a multi-region input-output (MRIO) database, for collecting data and Multi-criteria decision making (MCDM) approach for eco-efficiency assessment. Besides, the present paper utilizes expert weighting strategies such as EPP, SAB, Harvard, and EQUAL for assigning relative significance to various environmental indicators. Primarily, the data sample represents the influence of environmental stressors like GHG emission, land use, energy use, material consumption, water consumption. The study expands through three major scenarios in terms of importance to the economic and environmental outcomes. As such, with three scenarios and four weighting strategies, twelve situations are considered for the purpose of the study. The study findings indicate that the eco-efficiency score for given weighting strategies concerning economic and environmental impact demonstrates a significant statistical difference. The countries like China, India, Russia, Mexico, and Turkey are worst performing while Switzerland, Japan, UK, Germany, and France are best performing in terms of eco-efficiency score. Finally, k-mean clustering algorithm has applied to rank the countries centered on eco-efficiency score and weighing strategie

Learning Qualitative Constraint Networks

Temporal and spatial reasoning is a fundamental task in artificial intelligence and its related areas including scheduling, planning and Geographic Information Systems (GIS). In these applications, we often deal with incomplete and qualitative information. In this regard, the symbolic representation of time and space using Qualitative Constraint Networks (QCNs) is therefore substantial. We propose a new algorithm for learning a QCN from a non expert. The learning process includes different cases where querying the user is an essential task. Here, membership queries are asked in order to elicit temporal or spatial relationships between pairs of temporal or spatial entities. During this acquisition process, constraint propagation through Path Consistency (PC) is performed in order to reduce the number of membership queries needed to reach the target QCN. We use the learning theory machinery to prove some limits on learning path consistent QCNs from queries. The time performances of our algorithm have been experimentally evaluated using different scenarios